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Fixing multiplication operator warning for extended nonlocal games. #824

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merged 2 commits into from
Oct 4, 2024
Merged

Fixing multiplication operator warning for extended nonlocal games. #824

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203150d
Fixing multiplication operator warning for extended nonlocal games.
vprusso Oct 3, 2024
93a2e01
Attempt to fix warning for NLG now.
vprusso Oct 3, 2024
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12 changes: 6 additions & 6 deletions toqito/nonlocal_games/extended_nonlocal_game.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,7 +112,7 @@ def unentangled_value(self) -> float:

rho = cvxpy.Variable((dim_x, dim_y), hermitian=True)

objective = cvxpy.Maximize(cvxpy.real(cvxpy.trace(p_win.conj().T * rho)))
objective = cvxpy.Maximize(cvxpy.real(cvxpy.trace(p_win.conj().T @ rho)))

constraints = [cvxpy.trace(rho) == 1, rho >> 0]
problem = cvxpy.Problem(objective, constraints)
Expand Down Expand Up @@ -194,7 +194,7 @@ def nonsignaling_value(self) -> float:
for x_in in range(alice_in):
for y_in in range(bob_in):
p_win += self.prob_mat[x_in, y_in] * cvxpy.trace(
self.pred_mat[:, :, a_out, b_out, x_in, y_in].conj().T * k_var[a_out, b_out, x_in, y_in]
self.pred_mat[:, :, a_out, b_out, x_in, y_in].conj().T @ k_var[a_out, b_out, x_in, y_in]
)

objective = cvxpy.Maximize(cvxpy.real(p_win))
Expand Down Expand Up @@ -328,7 +328,7 @@ def __optimize_alice(self, bob_povms) -> tuple[dict, float]:
)
.conj()
.T
* rho[x_ques, a_ans]
@ rho[x_ques, a_ans]
)
if isinstance(
bob_povms[y_ques, b_ans],
Expand All @@ -343,7 +343,7 @@ def __optimize_alice(self, bob_povms) -> tuple[dict, float]:
)
.conj()
.T
* rho[x_ques, a_ans]
@ rho[x_ques, a_ans]
)
objective = cvxpy.Maximize(cvxpy.real(win))
constraints = []
Expand Down Expand Up @@ -396,7 +396,7 @@ def __optimize_bob(self, rho) -> tuple[dict, float]:
bob_povms[y_ques, b_ans],
)
)
* rho[x_ques, a_ans].value
@ rho[x_ques, a_ans].value
)
objective = cvxpy.Maximize(cvxpy.real(win))

Expand Down Expand Up @@ -455,7 +455,7 @@ def commuting_measurement_value_upper_bound(self, k: int | str = 1) -> float:
for y_in in range(bob_in):
p_win += self.prob_mat[x_in, y_in] * cvxpy.trace(
self.pred_mat[:, :, a_out, b_out, x_in, y_in].conj().T
* mat[x_in, y_in][
@ mat[x_in, y_in][
a_out * referee_dim : (a_out + 1) * referee_dim,
b_out * referee_dim : (b_out + 1) * referee_dim,
]
Expand Down
8 changes: 3 additions & 5 deletions toqito/nonlocal_games/nonlocal_game.py
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Original file line number Diff line number Diff line change
Expand Up @@ -363,7 +363,6 @@ def __optimize_alice(self, dim, bob_povms) -> tuple[dict, float]:
# .. math::
# \sum_{(x,y) \in \Sigma} \pi(x, y) V(a,b|x,y) \ip{B_b^y}{A_a^x}
win = 0
is_real = True
for x_ques in range(num_inputs_alice):
for y_ques in range(num_inputs_bob):
for a_ans in range(num_outputs_alice):
Expand All @@ -372,17 +371,16 @@ def __optimize_alice(self, dim, bob_povms) -> tuple[dict, float]:
win += (
self.prob_mat[x_ques, y_ques]
* self.pred_mat[a_ans, b_ans, x_ques, y_ques]
* cvxpy.trace(bob_povms[y_ques, b_ans].conj().T * alice_povms[x_ques, a_ans])
* cvxpy.trace(bob_povms[y_ques, b_ans].conj().T @ alice_povms[x_ques, a_ans])
)
if isinstance(
bob_povms[y_ques, b_ans],
cvxpy.expressions.variable.Variable,
):
is_real = False
win += (
self.prob_mat[x_ques, y_ques]
* self.pred_mat[a_ans, b_ans, x_ques, y_ques]
* cvxpy.trace(bob_povms[y_ques, b_ans].value.conj().T * alice_povms[x_ques, a_ans])
* cvxpy.trace(bob_povms[y_ques, b_ans].value.conj().T @ alice_povms[x_ques, a_ans])
)

objective = cvxpy.Maximize(cvxpy.real(win))
Expand Down Expand Up @@ -430,7 +428,7 @@ def __optimize_bob(self, dim, alice_povms) -> tuple[dict, float]:
win += (
self.prob_mat[x_ques, y_ques]
* self.pred_mat[a_ans, b_ans, x_ques, y_ques]
* cvxpy.trace(bob_povms[y_ques, b_ans].H * alice_povms[x_ques, a_ans].value)
* cvxpy.trace(bob_povms[y_ques, b_ans].H @ alice_povms[x_ques, a_ans].value)
)

objective = cvxpy.Maximize(cvxpy.real(win))
Expand Down